The majority of the existing studies on timber price forecasting are based on ARIMA/SARIMA autoregressive moving average models, while vector autoregressive (VAR) and exponential smoothing (ETS) models have been employed less often. To date, timber prices in primary timber markets have not been forecasted with ANN methodology. This methodology was used only for forecasting lumber futures. Low-labor-intensive and relatively simple solutions that can be used in practice as a tool supporting decisions of timber market participants were sought. The present work sets out to compare RBF and MLP artificial neural networks with the Prophet procedure and with classical models (i.e., ARIMA, ETS, BATS, and TBATS) in terms of their suitability for forecasting timber prices in Poland. The study material consisted of quarterly time series of net nominal prices of roundwood (W0) for the years 2005–2021. MLP was found to be far superior to other models in terms of forecasting price changes and levels. ANN models exhibited a better fit to minimum and maximum values as compared to the classical models, which had a tendency to smooth price trends and produce forecasts biased toward average values. The Prophet procedure led to the lowest quality of projections. Ex-post error-based measures of prediction accuracy revealed a complex picture. The best forecasts for alder wood were obtained using the ETS model (with RMSE and MAE values of approx. 0.38 € m⁻³). ETS also performed well with respect to beech timber, although in this case BATS was just as good in terms of RMSE, while the difference between ETS and neural models amounted to as little as 0.64 € m⁻³. Birch timber prices were most accurately predicted with BATS and TBATS models (MAE 0.86 € m⁻³, RMSE 1.04 € m⁻³). The prices of the most popular roundwood types in Poland, i.e., Scots pine, Norway spruce, and oaks, were best forecasted using ANNs, and especially MLP models. Among the neural models for oak (MAE 4.74 € m⁻³, RMSE 8.09 € m⁻³), pine (MAE 2.21 € m⁻³, RMSE 2.83 € m⁻³), beech (MAE 2.31 € m⁻³, RMSE 2.70 € m⁻³), alder (MAE 1.88 € m⁻³, RMSE 2.40 € m⁻³), and spruce (MAE 2.44 € m⁻³, RMSE 2.58 € m⁻³), the MLP model was the best (the RBF model for birch). Of the seven models used to forecast the prices of six types of wood, the worst results were obtained for oak wood, while the best results were obtained for alder.

The article examines the potential utilization of forest biomass in bioenergy production in Europe, taking into account limiting and developmental factors. The methodology includes a strategic analysis and the use of PEST analysis to evaluate the market for wood biomass. In the context of the current geopolitical situation and the decarbonization goals of the EU, the authors recommend accelerating energy transformation and highlighting forest biomass as an alternative within renewable energy sources. A literature review indicates the need to revise EU assumptions to enable the use of wood for bioenergy production, taking into account the needs of the wood industry. The analysis of economic factors shows competitiveness of forest biomass against coal, yet challenges arise regarding resource availability and competition with other energy sources. Emphasis is placed on the necessity of sustainable forest resource management and technological innovation. In the context of an energy crisis, the article underscores the role of innovation and recycling in alleviating shortages in energy markets. Conclusions highlight the imperative to develop a sustainable energy strategy for forest resource management and engage EU countries in the development of new biofuel and renewable energy sources for energy security and environmental protection.

The utilization of primary and secondary woody biomass resources, despite controversies, is being promoted to reduce dependence on fossil fuels and due to the need to diversify energy sources and ensure energy security in European Union countries. Forest biomass is one of the renewable and sustainable energy sources that can be used for electricity, heat, and biofuel production. In the context of the ongoing energy crisis in Europe, an attempt was made to analyze the production and consumption of woody biomass for energy purposes (fuel wood, chips, and pellets). Specifically, an analysis of similarities between European countries in terms of biomass utilization was conducted. The analysis was complemented by a forecast of primary biomass production in selected European countries. The similarity analysis was conducted using the Ward method. Artificial neural networks (ANNs), including multi-layer feedforward perceptron (MLP) and radial basis function (RBF) models, were used to predict fuelwood extraction. The study showed that woody biomass remains an important source of bioenergy in Europe, and its significance as a strategic resource guaranteeing energy security is likely to increase. Fuel wood harvesting in Europe generally shows an upward trend, particularly in the Czech Republic, Germany, Estonia, Denmark, and the UK. A decreasing trend was observed in France, Spain, Greece, and Cyprus. The analysis revealed differences between countries in terms of woody biomass consumption. The ANN-based forecasts of fuelwood supply generally showed an increase in primary biomass harvesting.

The European beech (Fagus sylvatica L.) is the most prevalent deciduous tree species in Central Europe. The implementation of sustainable, close-to-nature silvicultural practices increased the percentage share of beech in forest species composition, raising the economic significance of beech roundwood, especially in terms of revenues from timber sales. The elucidation of roundwood price mechanisms as well as long-term equilibrium relations between international markets may be helpful in calculating the profitability of beech roundwood production. The study material consists of quarterly time series of beech roundwood prices from Austria, Czechia, Germany, Poland, Slovakia, and Slovenia in the years 2005–2018. The price time series were described with a multiplicative model incorporating seasonal, cyclical, and irregular, as well as long-term trend components. The time series were decomposed using the Census X11 method. Stationarity was tested by means of the augmented Dickey–Fuller test (ADF) and the Kwiatkowski–Phillips–Schmidt–Shin test (KPSS). Cointegration was assessed using the Johansen and Engle–Granger methods. From 2005–2018, the highest mean beech roundwood prices were found for Austria (77.5 € m⁻³) and Germany, and the lowest for Poland and Slovakia. Roundwood prices were badly affected by the 2008/2009 financial crisis, which caused an approx. 27% decline. The prices of large-diameter beech logs exhibited seasonal fluctuations, typically reaching a maximum in Q1 and a minimum in Q3. The amplitude of those fluctuations was the highest in Slovenia in 2005 (10.1%), while in Czechia and Germany, seasonal effects increased over the period of study. The lowest seasonality was found in Slovakia and Austria (in the latter country it was not statistically significant). On an annual scale, cyclical changes generally accounted for the largest proportion of price variation, and were particularly pronounced in Poland (78.9%), Slovakia (78.6%), and Austria (69.2%). On the other hand, seasonal effects were predominant in the Slovenian (40.6%), German (34.1%), and Czech (33.3%) markets. In countries with price series of type I(0), simple correlation between stationary beech roundwood prices is positive and the strongest between Czechia–Poland and Czechia–Austria; on the other hand they are the weakest in the German market. In Slovakia and Slovenia with nonstationary price series, both Johansen’s and Engle-Granger’s cointegration tests indicated the absence of a long-term equilibrium between the analyzed beech timber markets. Results revealed integration between the prices of large-diameter beech logs in Czechia, Austria, and Poland. It should be mentioned that in this study, the time series of price used are rather short for long time cointegration analysis, which might prevent the proper detection of cointegration between all analyzed countries.

The objective of the study was to verify the applicability and usefulness of time series decomposition in analyzing the variability of timber prices and supply in Poland. The employed multiplicative model was the product of four components: cyclical, seasonal, and irregular fluctuations and the long-term trend. The elements of the time series were determined by means of the Census X11 method, while cyclicality was separated from the trend employing the Hodrick–Prescott filter. Data included quarterly information about the supply (volume) and prices (value) of the timber sold by the State Forests in the years 2005–2018. Analyses were performed for tree species with the greatest economic significance, that is, pine, oak, spruce, beech, birch, and alder, and for their most popular assortments: general purpose large-diameter timber (W0) and medium-diameter timber (S2A). Time series decomposition of quarterly timber production volume and prices revealed irregular, seasonal, and cyclic fluctuations. Within an annual time horizon, irregular fluctuations accounted on average for 6.7% and 28% of overall variability in timber prices and supply, respectively; they exhibited low amplitudes (+5%, −25%, respectively). Cyclical fluctuations were primarily found for prices and were characterized by substantial variations in cycle length (2–4 years) and change amplitude (3–27 Euros). Cyclical fluctuations in timber prices and supply were usually negatively correlated with each other: the upper turning points of price cycles fell near the lower turning points of supply cycles (with a shift of 1 to 3 quarters). The seasonality of prices was also inversely correlated with supply: quarters with low supply exhibited higher prices and vice versa. Seasonal fluctuations were more pronounced for timber supply (36%) as compared to timber prices (20.3%). Different seasonality patterns were found for hardwood and softwood. The lowest supply of softwood was found in the first quarter and the highest in the third quarter (spruce) or fourth quarter (pine). The supply of hardwood was the highest in the first quarter and the lowest in the third quarter.

Regulation (EU) 2020/852 of the European Parliament, also known as the Taxonomy Regulation, facilitates environmentally sustainable investments. It is part of the concept of the European Green Deal and a ‘tool’ for financial institutions, enterprises, and investors, facilitating the assessment of the environmental impact of a particular project. The Regulation contains the criteria an activity must meet to be considered environmentally sustainable. The role of the Taxonomy Regulation is to enable the flow of public and private capital towards ecological and sustainable activities. The document does not need to be implemented into the legal order of individual EU member-states, which results in its direct application. The main financial instruments enabling the achievement of the goals of the Taxonomy Regulation may be green bonds and other forms of capital raising by entrepreneurs and forest ownership structures. The assumption of the Regulation is to achieve the principles of sustainable environmental activity when spending funds obtained from private investors. It is an issue of key significance to identify the areas of management and financial accounting in the operational activities of forest enterprises that can be qualified for the Taxonomy Regulation. Forestry activities, including the processes mentioned therein, the objectives of the New EU Forest Strategy, and the LULUCF Regulation, are to play an essential role in reducing greenhouse gas emissions. The role of forestry in the supply chain in its broad sense is also considered. Forestry and forest management can receive capital for sustainable development due to the threat resulting from exclusions that strengthen the protective function of the forest (the protection of biodiversity). These processes will occur at the expense of production and numerous social functions.